Background: The rapid development and complexity of new x-ray computed tomography (CT) technologies and the need for evidence-based optimization of image quality with respect to radiation and contrast media dose call for an updated approach towards CT performance evaluation. Aims: This report offers updated testing guidelines for testing CT systems with an enhanced focus on the operational performance including iterative reconstructions and automatic exposure control (AEC) techniques. Materials and Methods: The report was developed based on a comprehensive review of best methods and practices in the scientific literature. The detailed methods include the assessment of 1) CT noise (magnitude, texture, nonuniformity, inhomogeneity), 2) resolution (task transfer function under varying conditions and its scalar reflections), 3) task-based performance (detectability, estimability), and 4) AEC performance (spatial, noise, and mA concordance of attenuation and exposure modulation). The methods include varying reconstruction and tube current modulation conditions, standardized testing protocols, and standardized quantities and metrology to facilitate tracking, benchmarking, and quantitative comparisons. Results: The methods, implemented in cited publications, are robust to provide a representative reflection of CT system performance as used operationally in a clinical facility. The methods include recommendations for phantoms and phantom image analysis. Discussion: In line with the current professional trajectory of the field toward quantitation and operational engagement, the stated methods offer quantitation that is more predictive of clinical performance than specification-based approaches. They can pave the way to approach performance testing of new CT systems not only in terms of acceptance testing (i.e., verifying a device meets predefined specifications), but also system commissioning (i.e., determining how the system can be used most effectively in clinical practice). Conclusion: We offer a set of common testing procedures that can be utilized towards the optimal clinical utilization of CT imaging devices, benchmarking across varying systems and times, and a basis to develop future performance-based criteria for CT imaging.

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ABCB1 encodes Multidrug Resistance protein (MDR1), an ATP-binding cassette member involved in the cellular efflux of chemotherapeutic drugs. Here we report that ovarian and breast samples from chemotherapy treated patients are positive for multiple transcriptional fusions involving ABCB1, placing it under the control of a strong promoter while leaving its open reading frame intact. We identified 15 different transcriptional fusion partners involving ABCB1, as well as patients with multiple distinct fusion events. The partner gene selected depended on its structure, promoter strength, and chromosomal proximity to ABCB1. Fusion positivity was strongly associated with the number of lines of MDR1-substrate chemotherapy given. MDR1 inhibition in a fusion positive ovarian cancer cell line increased sensitivity to paclitaxel more than 50-fold. Convergent evolution of ABCB1 fusion is therefore frequent in chemotherapy resistant recurrent ovarian cancer. As most currently approved PARP inhibitors (PARPi) are MDR1 substrates, prior chemotherapy may precondition resistance to PARPi.

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Purpose In response to the increased risk of radiological terrorist attack, a network of Centers for Medical Countermeasures against Radiation (CMCR) has been established in the United States, focusing on evaluating animal model responses to uniform, relatively homogenous whole- or partial-body radiation exposures at relatively high dose rates. The success of such studies is dependent not only on robust animal models but on accurate and reproducible dosimetry within and across CMCR. To address this issue, the Education and Training Core of the Duke University School of Medicine CMCR organised a one-day workshop on small animal dosimetry. Topics included accuracy in animal dosimetry accuracy, characteristics and differences of cesium-137 and X-ray irradiators, methods for dose measurement, and design of experimental irradiation geometries for uniform dose distributions. This paper summarises the information presented and discussed. Conclusions Without ensuring accurate and reproducible dosimetry the development and assessment of the efficacy of putative countermeasures will not prove successful. Radiation physics support is needed, but is often the weakest link in the small animal dosimetry chain. We recommend: (i) A user training program for new irradiator users, (ii) subsequent training updates, and (iii) the establishment of a national small animal dosimetry center for all CMCR members.

MD Purpose:To compare five methodologies the American Association of Physicists in Medicine Report 204 used to calculate size-specific dose estimates (SSDEs) for pediatric computed tomography (CT). Materials andMethods:The institutional review board waived consent for this HIPAAcompliant retrospective study. The five SSDE methodologies were investigated for calculation variation based on volumetric CT dose index (CTDI), or CTDI vol , of chest, abdominal, and pelvic CT. SSDE calculations were derived from a predominantly pediatric population of 186 patients retrospectively and consecutively analyzed from June through November 2011. Eighty (43%) of the 186 patients were female, and 106 (57%) were male. Mean patient age was 8.6 years 6 6.3 (standard deviation), the age range was 1 month to 28 years, and mean weight was 37.7 kg 6 33.1, with a range of 3.4-146.6 kg. SSDE conversion factors were derived from anteroposterior (AP) and lateral dimensions measured on the patient's CT radiograph. The measurements were either used independently, or as a summation, or to calculate the patient's effective diameter; additionally, SSDE was derived on the basis of the patient's age (International Commission on Radiation Units Report 74 data). SSDE conversion factors were applied to CTDI vol data that corrected for both 16-and 32-cm-diameter CTDI phantom measurements. SSDE data were summarized by using the patient's originally prescribed weightbased CT scanning protocols. Data were summarized by using descriptive statistics. Results:SSDEs derived from individual measurements varied 2%-12%. The combination of measurements (sum or effective diameter) varied 0.9%-2%. The age approach varied by an average of 2% (in the younger population [0-13 years]), but up to 44%, with an average of 18% (in the older population [14-18 years]). No SSDE correction was required for patients of varying size who weighed 36 kg or less when CTDI vol was measured by using a 16-cm CTDI phantom or for patients weighing 100-140 kg when CTDI vol was measured by using a 32-cm phantom. CTDI vol measured by using a 32-cm phantom in patients weighing between 36 and 100 kg and patients weighing more than 140 kg differed from SSDE by an average of 35%. An average difference of 1% was found between male and female SSDE-corrected values when the two sexes were compared within the same CT weight scanning categories. Conclusion:The combination of AP and lateral measurements should be used to determine SSDE correction factors when possible. For pediatric patients, CTDI vol calculated with a 32-cm phantom requires SSDE conversion to more accurately estimate patient dose; CTDI vol calculated with a 16-cm phantom for pediatric patients weighing 36 kg or less does not require SSDE conversion.q RSNA, 2012

Current innovation in computed tomography (CT) is focused on radiomics, patient-specific radiation dose calculation, and image quality improvement using iterative reconstruction, all of which require specific knowledge of tissue and organ systems within a CT image. The purpose of this study was to develop a fully automated Random Forest classifier algorithm for segmentation of neck-chest-abdomen-pelvis CT examinations based on pediatric and adult CT protocols. Seven materials were classified: background, lung/internal air or gas, fat, muscle, solid organ parenchyma, blood/contrast enhanced fluid, and bone tissue using Matlab and the Trainable Weka Segmentation (TWS) plugin of FIJI. The following classifier feature filters of TWS were investigated: minimum, maximum, mean, and variance evaluated over a voxel radius of 2n, (n from 0 to 4), along with noise reduction and edge preserving filters: Gaussian, bilateral, Kuwahara, and anisotropic diffusion. The Random Forest algorithm used 200 trees with 2 features randomly selected per node. The optimized auto-segmentation algorithm resulted in 16 image features including features derived from maximum, mean, variance Gaussian and Kuwahara filters. Dice similarity coefficient (DSC) calculations between manually segmented and Random Forest algorithm segmented images from 21 patient image sections, were analyzed. The automated algorithm produced segmentation of seven material classes with a median DSC of 0.86 ± 0.03 for pediatric patient protocols, and 0.85 ± 0.04 for adult patient protocols. Additionally, 100 randomly selected patient examinations were segmented and analyzed, and a mean sensitivity of 0.91 (range: 0.82–0.98), specificity of 0.89 (range: 0.70–0.98), and accuracy of 0.90 (range: 0.76–0.98) were demonstrated. In this study, we demonstrate that this fully automated segmentation tool was able to produce fast and accurate segmentation of the neck and trunk of the body over a wide range of patient habitus and scan parameters.

For organs fully covered within the scan volume, the average correlation of SSDE and organ absolute dose was found to be better than ± 10%. In addition, this study provides a complete list of organ dose correlation factors (CF(organ)(SSDE)) for the chest and abdominopelvic regions, and describes a simple methodology to estimate individual pediatric patient organ dose based on patient SSDE.